Module ACCT42515: Introduction to Machine Learning & Artificial Intelligence

Department: Accounting

ACCT42515: Introduction to Machine Learning & Artificial Intelligence

Prerequisites

• None

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• This module will aim to introduce students to the concepts, terminologies, tools of machine learning and Artificial Intelligence (AI). In particular, on the successful completion of this module students will be able to:
• understand the theoretical foundation of machine learning
• apply appropriate supervised and unsupervised machine learning techniques to analyse big data sets
• utilise statistical packages and software tools (e.g. MATLAB / Octave / Python) to develop big data and machine learning models
• design and analyse machine learning experiments

Content

• Machine learning and Artificial Intelligence (AI) in business, finance, accounting and auditing practices
• Linear regression
• Logistic regression
• Regularisation
• Introduction to Artificial Neural Networks (ANNs)
• Association rules
• Clustering with K-Means
• Dimensionality reduction
• Anomaly detection
• AI Ethics and Bias

Learning Outcomes

• By the end of the module students should be able to show:
• demonstration of different the evolution of machine learning and AI;
• identify different applications of machine learning and AI in the accounting and audit profession;
• understanding of the application of supervised and unsupervised machine learning;
• clear understanding of different data behaviour and the appropriate analytics modelling techniques;
• demonstration of advanced knowledge and understanding of the implementation of AI techniques to resolve accounting and audit problems and challenges;
• discussion of key ethical considerations and challenges which emerge when training, assessing and using machine learning and AI models.

• By the end of the module students should be:
• competent in data coding, data evaluation and data applications in business, finance, accounting and auditing;
• competent in manipulating data and programming;
• capable of implementing different machine learning and AI tools to develop a systematic modelling network.

• Data analytics and visualisation skills.
• The ability to communicate effectively: communicating complex ideas.
• The ability to think critically and creatively and to argue coherently.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• The module is delivered via online learning, divided up into study weeks with specially produced resources within each week. Resources vary according to the learning outcomes but normally include: video content, directed reading, reflective activities, opportunities for self-assessment and live scheduled webinars. The hours as depicted in the Teaching Methods and Learning Hours table are indicative.
• The formative assessment serves to encourage students to study regularly and to monitor their learning progress. Tutors provide feedback on formative work and are available for individual consultation as necessary (usually by email and Zoom or Microsoft Teams).
• The summative assessment of the module is designed to test the acquisition and articulation of knowledge and critical understanding, and skills of application and interpretation within the accounting and audit context.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

Students undertake a series of activities aligned to the module content, receiving ongoing feedback on the theoretical knowledge and how it is applied.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University